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-edge advancements in automated drug discovery through the integration of high data-density reaction/bioanalysis techniques, organic synthesis, laboratory automation & robotics and machine learning
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is, in general, a challenging problem and one for which there is relatively little data, because experimental chemistry takes time and is expensive. Within our research group, we have a highly
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piloting an interventional clinical trial at Nottingham University Hospitals NHS Trust. Patients will be randomised to receive either standard of care (which includes a patient information sheet) or standard
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. Boiling is a technology central to both fusion and fission nuclear reactors, also including thermal management of several reactor components. The aim of these simulations is to generate data that can be
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that present significant problems for data reduction and analysis. This PhD project, in close collaboration with researchers at ISIS Neutron and Muon Source and University of Nottingham, will address this by
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key area of patient safety that can be improved with the use of computer vision approaches to system analysis. For many clinical procedures there can be multiple deviations in service delivery, which
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environment. How to apply: Applicants are welcome to contact Professor Yupeng (Jack) Wu Yupeng.Wu@nottingham.ac.uk directly for more information about the projects. Application will need to email your CV
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amounts of data required for effective monitoring. This data-driven approach can result in high costs, increased computational demands, and logistical challenges in data storage and processing. A crucial
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robots. We currently imagine these to be soft materials that dress both human and robot bodies. Embedded sensors will capture data about mutual touch. Embedded actuators will deliver haptic feedback
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cohort to benefit from peer-to-peer learning and transferable skills development. For full information about the programme, the research projects available and how to apply, please visit: http